Therefore we format our RCNN structure as shown in Fig. REF .
The \(E\times E\times 2\)  input goes through two parallel CNN layers, followed by a ReLu activation layer used for helps to prevent the exponential growth in the computation, a batch normalization that makes the optimization landscape significantly smoother by using normalizing smaller mini batch, a maxpool layer to progressively reduce the spatial size of the latent space, and dropout layers which prevent a model from overfitting and robust to outliers.
Since the use of these latter three are very common [1]}, we skip their visualization.
Note that the symmetric kernel on each branch is used to aggregate the adjacent samples along both horizontal and vertical axes to provide similar learning efficiencies [2]}.
These two branches are later merged into a \(6\times 6\times 16\)  space, which is fed directly into another CNN layer.
In particular, residual connectivity is implemented via a direct path parallel to this CNN layer, summed to the CNN output via element-wise addition.
This regulates the learning as shown in [3]}.
This technique helps avoid back-propagation gradient degradation and performance saturation in sequentially stacked layers.
The next layer consists of two grouped conv layers with asymmetric kernel sizes.
The final processing consists of flattening into one dimension.
We use a version of cross entropy weighted for classification problems, and the Adam optimizer.
